An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines

Yang, Jianchen; Virostko, John; Hormuth, David A.; Liu, Junyan; Brock, Amy; Kowalski, Jeanne; Yankeelov, Thomas E.

doi:10.1371/journal.pone.0240765

Cited by 11 publications

(25 citation statements)

References 55 publications

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“…The simulations are conducted for 5 mM glucose concentration with the initial confluences of dead and live cells of 0.5 and 0.3, respectively. In the in vitro experiments, the average initial dead cell confluence (i.e., the fraction of the domain occupied by dead cells) is 0.029 ± 0.005, and the average percentage of dead cells at day = 0 is 6.5 ± 0.7% [ 73 ]. (We note that the cell viability in a healthy cell culture should be 80–95%; thus, the percentage of dead cells in our experiments is aligned with expectations [ 74 ].)…”

Section: Resultsmentioning

confidence: 99%

Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth

et al. 2021

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Hybrid multiscale agent-based models (ABMs) are unique in their ability to simulate individual cell interactions and microenvironmental dynamics. Unfortunately, the high computational cost of modeling individual cells, the inherent stochasticity of cell dynamics, and numerous model parameters are fundamental limitations of applying such models to predict tumor dynamics. To overcome these challenges, we have developed a coarse-grained two-scale ABM (cgABM) with a reduced parameter space that allows for an accurate and efficient calibration using a set of time-resolved microscopy measurements of cancer cells grown with different initial conditions. The multiscale model consists of a reaction-diffusion type model capturing the spatio-temporal evolution of glucose and growth factors in the tumor microenvironment (at tissue scale), coupled with a lattice-free ABM to simulate individual cell dynamics (at cellular scale). The experimental data consists of BT474 human breast carcinoma cells initialized with different glucose concentrations and tumor cell confluences. The confluence of live and dead cells was measured every three hours over four days. Given this model, we perform a time-dependent global sensitivity analysis to identify the relative importance of the model parameters. The subsequent cgABM is calibrated within a Bayesian framework to the experimental data to estimate model parameters, which are then used to predict the temporal evolution of the living and dead cell populations. To this end, a moment-based Bayesian inference is proposed to account for the stochasticity of the cgABM while quantifying uncertainties due to limited temporal observational data. The cgABM reduces the computational time of ABM simulations by 93% to 97% while staying within a 3% difference in prediction compared to ABM. Additionally, the cgABM can reliably predict the temporal evolution of breast cancer cells observed by the microscopy data with an average error and standard deviation for live and dead cells being 7.61±2.01 and 5.78±1.13, respectively.

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Section: Resultsmentioning

confidence: 99%

Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth

et al. 2021

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“…To the best of our knowledge, this work represents the first study to rigorously calibrate an agent-based model of tumor angiogenesis with multimodal experimental data to forecast vascular growth that is then directly compared to the corresponding data. Importantly, other efforts have pioneered the integration of data in other ways [63,64]. For example, Perfahl et al employed multiphoton microscopy to image angiogenic vasculature in an in vivo dorsal skin fold chamber [7].…”

Section: Discussionmentioning

confidence: 99%

Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis

Phillips

Eabf

Gadde

et al. 2021

Preprint

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The goal of this study is to calibrate a multiscale model of tumor angiogenesis with time-resolved data to allow for systematic testing of mathematical predictions of vascular sprouting. The multi-scale model consists of an agent-based description of tumor and endothelial cell dynamics coupled to a continuum model of vascular endothelial growth factor concentration. First, we calibrate ordinary differential equation models to time-resolved protein expression data to estimate the rates of secretion and consumption of vascular endothelial growth factor by endothelial and tumor cells, respectively. These parameters are then input into the multiscale tumor angiogenesis model, and the remaining model parameters are then calibrated to time resolved confocal microscopy images obtained within a 3D vascularized microfluidic platform. The microfluidic platform mimics a functional blood vessel with a surrounding collagen matrix seeded with inflammatory breast cancer cells, which induce tumor angiogenesis. Once the multi-scale model is fully parameterized, we forecast the spatiotemporal distribution of vascular sprouts at future time points and directly compare the predictions to experimentally measured data. We assess the ability of our model to globally recapitulate angiogenic vasculature density, resulting in an average relative calibration error of 17.7% ± 6.3% and an average prediction error of 20.2% ± 4% and 21.7% ±3.6% using one and four calibrated parameters, respectively. We then assess the model's ability to predict local vessel morphology (individualized vessel structure as opposed to global vascular density), initialized with the first time point and calibrated with two intermediate time points. To the best of our knowledge, this represents the first study to integrate well-controlled, experimental data into a mechanism-based, multiscale, mathematical model of angiogenic sprouting to make specific, testable predictions.

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“…Overall, these models have been able to simulate experimental data and reveal which type of metabolic response best fits the available results. Furthermore, with the constant increase in data availability, models are evolving to become patient-specific [112] , [113] . However, many of the models developed until now are focused on a single scale and there is still a need for fully coupled models that integrate biochemical networks with cellular and extracellular behaviour.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Hybrid computational models of multicellular tumour growth considering glucose metabolism

Gonçalves

García-Aznar

2023

Computational and Structural Biotechnology Journal

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An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines

Cited by 11 publications

References 55 publications

Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth

Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth

Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis

Hybrid computational models of multicellular tumour growth considering glucose metabolism

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